clear all

load ParEst_eh0_time0_Mex_girls ParEst_eh0_time0_Mex_girls
load W_time1_age0_eh0_comp11.out
load W_time1_age0_eh0_comp1.out
load W_time1_eh0_comp1.out
load stock_time1_age0_eh0_comp1.out
load stock_time1_eh0_comp1.out
load trans_time1_age0_eh0_comp1.out
load trans_time1_eh0_comp1.out

Par1 = zeros(13,1);
Par1(1:13) = ParEst_eh0_time0_Mex_girls;
Par1(11) = 10^Par1(11);
Par1(12:13)=1000*Par1(12:13);

% Parameters labels - men
df_2=Par1(1);
di_2=Par1(2);
lnf_2=Par1(3);
lni_2=Par1(4);
lfi_2=Par1(5);
lif_2=Par1(6);
alphaf=Par1(7);
betaf=Par1(8);
alphai=Par1(9);
betai=Par1(10);
theta=Par1(11)
a=Par1(12)
gamma=0; % gamma = 0 at benchmark
b2 = Par1(13)

w_f_g=W_time1_age0_eh0_comp11; % Read wages, g and f distributions
wf_1=w_f_g(:,1);        % Wage in formal sector
wi_1=w_f_g(:,2);        % Wage in informal sector
wf_2=w_f_g(:,7);      % Wage in formal sector
wi_2=w_f_g(:,8);      % Wage in informal sector

w_mom=W_time1_age0_eh0_comp1; % Read wage moments
meanwi_1=w_mom(1); meanwf_1=w_mom(2);   meanwi_2=w_mom(3);  meanwf_2=w_mom(4);  sdwi_1=w_mom(5); sdwf_1=w_mom(6);  sdwi_2=w_mom(7);   sdwf_2=w_mom(8);
p10wi_1=w_mom(9);  p10wf_1=w_mom(10); p10wi_2=w_mom(11); p10wf_2=w_mom(12); p25wi_1=w_mom(13); p25wf_1=w_mom(14); p25wi_2=w_mom(15); p25wf_2=w_mom(16); 
p50wi_1=w_mom(17); p50wf_1=w_mom(18); p50wi_2=w_mom(19); p50wf_2=w_mom(20); p75wi_1=w_mom(21); p75wf_1=w_mom(22); p75wi_2=w_mom(23); p75wf_2=w_mom(24); 
p90wi_1=w_mom(25); p90wf_1=w_mom(26); p90wi_2=w_mom(27); p90wf_2=w_mom(28); minwi_1=w_mom(29); minwf_1=w_mom(30); minwi_2=w_mom(31); minwf_2=w_mom(32); 
maxwi_1=w_mom(33); maxwf_1=w_mom(34); maxwi_2=w_mom(35); maxwf_2=w_mom(36);
wage_moments_data=[meanwi_2; meanwf_2; sdwi_2; sdwf_2; ...
    p10wi_2; p10wf_2;  p25wi_2; p25wf_2; ...
    p50wi_2; p50wf_2;  p75wi_2; p75wf_2; ...
    p90wi_2; p90wf_2]; % girls

D_mom=trans_time1_age0_eh0_comp1; % Read transition moments
Dij=D_mom';

stock_mom=stock_time1_age0_eh0_comp1; % Read stocks
stock_ANT=stock_mom'; 
stock = [stock_ANT(1) + stock_ANT(4) + stock_ANT(5); stock_ANT(2) + stock_ANT(6) + stock_ANT(8); stock_ANT(3) + stock_ANT(7) + stock_ANT(9)]; % girls

% Transitions
Dnf_2	 =	Dij(9);
Dni_2	 =	Dij(10);
Dfn_2	 =	Dij(11);
Dff_2	 =	0;
Dfi_2	 =	Dij(12);
Din_2	 =	Dij(13);
Dii_2	 =	0;
Dif_2  =	Dij(14);

HHstates_data=stock;
transitions_data=[Dfn_2;Din_2;Dnf_2;Dni_2;Dfi_2;Dif_2];

n=50;
 % solve dist of wage offers, value functions and simulate trajectory
[Ff_2,ff_2,Fi_2,fi_2]=solve_wage_offer_dist(Par1,wf_2,wi_2,n);
[Wf,Wi,Wn,iterVF]=solve_value_function_A1(Par1,Ff_2,wf_2,Fi_2,wi_2,n);
[HHstates,wage_moments,transitions]=data_simulation_A12_end(Par1,ff_2,wf_2,fi_2,...
    wi_2,Wf,Wi,Wn);

%Contact rates -  spouse
Transitions_spouse = {'F-N'; 'I-N'; 'N-F'; 'N-I'; 'F-I'; 'I-F'};
Estimates = [df_2; di_2; lnf_2; lni_2; lfi_2; lif_2];
Transition_Rates_table = table(Transitions_spouse, Estimates);
writetable(Transition_Rates_table, 'Transition_Rates_table.xls')

HH_avg_Inc = p50wi_2; % median wage of informal men
MWP_a = a/(exp(-theta*HH_avg_Inc));
save MWP_a MWP_a

%Preference parameters
Preference = {'theta';'b2';'a';'MWP a'};
Estimates = [theta; b2; a; MWP_a];
Preferences_table = table(Preference, Estimates);
writetable(Preferences_table, 'Preferences_table.xls')

% Generate mean LOG WAGES - F distribution 
mean_wf_2f=log(sum(wf_2.*ff_2));
mean_wi_2f=log(sum(wi_2.*fi_2));
mean_wage_offer=[mean_wf_2f;mean_wi_2f];
save mean_wage_offer mean_wage_offer

%Wage offer parameters
Wage_offer = {'alpha_formal'; 'beta_formal';'alpha_informal';'beta_informal';'';'mean_wf_2f';'mean_wi_2f'};
Estimates = [alphaf; betaf; alphai; betai;NaN;mean_wage_offer];
Wage_offer_par_table = table(Wage_offer, Estimates);
writetable(Wage_offer_par_table, 'Wage_offer_par_table.xls')

% Model fit
y=transitions_data;
transitions_data1=[y(3);y(4);y(1);y(5);y(2);y(6)];
z=transitions;
transitions1=[z(3);z(4);z(1);z(5);z(2);z(6)];

% Model fit (for paper)
Model_fit_labels = {'m_f';'m_i';'m_n';'';'';...
    'Dnf';'Dni';'Dfn';'Dfi';'Din';'Dif'};
ModelFit_actual=[HHstates_data;NaN;NaN;transitions_data1];
ModelFit_model=[HHstates;NaN;NaN;transitions1];
Model_fit_table = table(Model_fit_labels,ModelFit_actual,ModelFit_model);
writetable(Model_fit_table, 'Model_fit_table_paper1.xls')

x=wage_moments_data;
wage_moments_data1=[x(2);x(4);NaN;NaN;x(1);x(3)];
w=wage_moments;
wage_moments1=[w(2);w(4);NaN;NaN;w(1);w(3)];

% Model fit (for paper)
Model_fit_labels = {'meanwf_2';'sdwf_2';'';'';...
    'meanwi_2';'sdwi_2'};
ModelFit_actual=log(wage_moments_data1);
ModelFit_model=log(wage_moments1);
Model_fit_table = table(Model_fit_labels,ModelFit_actual,ModelFit_model);
writetable(Model_fit_table, 'Model_fit_table_paper2.xls')
